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import gradio as gr
import os
import torch
import numpy as np
import pandas as pd
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
from huggingface_hub import HfApi
from label_dicts import ILLFRAMES_MIGRATION_LABEL_NAMES, ILLFRAMES_COVID_LABEL_NAMES, ILLFRAMES_WAR_LABEL_NAMES
from .utils import is_disk_full, release_model
HF_TOKEN = os.environ["hf_read"]
languages = [
"English"
]
domains = {
"Covid": "covid",
"Migration": "migration",
"War": "war"
}
# --- DEBUG ---
import shutil
def convert_size(size):
for unit in ['B', 'KB', 'MB', 'GB', 'TB', 'PB']:
if size < 1024:
return f"{size:.2f} {unit}"
size /= 1024
def get_disk_space(path="/"):
total, used, free = shutil.disk_usage(path)
return {
"Total": convert_size(total),
"Used": convert_size(used),
"Free": convert_size(free)
}
# ---
def check_huggingface_path(checkpoint_path: str):
try:
hf_api = HfApi(token=HF_TOKEN)
hf_api.model_info(checkpoint_path, token=HF_TOKEN)
return True
except:
return False
def build_huggingface_path(domain: str):
return f"poltextlab/xlm-roberta-large-english-ILLFRAMES-{domain}"
def predict(text, model_id, tokenizer_id, label_names):
device = torch.device("cpu")
# Load JIT-traced model
jit_model_path = f"/data/jit_models/{model_id.replace('/', '_')}.pt"
model = torch.jit.load(jit_model_path).to(device)
model.eval()
# Load tokenizer (still regular HF)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
# Tokenize input
inputs = tokenizer(
text,
max_length=256,
truncation=True,
padding="do_not_pad",
return_tensors="pt"
)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
output = model(inputs["input_ids"], inputs["attention_mask"])
print(output) # debug
logits = output["logits"]
release_model(model, model_id)
probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()
NUMS_DICT = {i: key for i, key in enumerate(sorted(label_names.keys()))}
output_pred = {f"[{NUMS_DICT[i]}] {label_names[NUMS_DICT[i]]}": probs[i] for i in np.argsort(probs)[::-1]}
output_info = f'<p style="text-align: center; display: block">Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.</p>'
return output_pred, output_info
def predict_illframes(text, language, domain):
domain = domains[domain]
model_id = build_huggingface_path(domain)
tokenizer_id = "xlm-roberta-large"
if domain == "migration":
label_names = ILLFRAMES_MIGRATION_LABEL_NAMES
elif domain == "covid":
label_names = ILLFRAMES_COVID_LABEL_NAMES
elif domain == "war":
label_names = ILLFRAMES_WAR_LABEL_NAMES
if is_disk_full():
os.system('rm -rf /data/models*')
os.system('rm -r ~/.cache/huggingface/hub')
return predict(text, model_id, tokenizer_id, label_names)
demo = gr.Interface(
title="ILLFRAMES Babel Demo",
fn=predict_illframes,
inputs=[gr.Textbox(lines=6, label="Input"),
gr.Dropdown(languages, label="Language", value=languages[0]),
gr.Dropdown(domains.keys(), label="Domain", value=list(domains.keys())[0])],
outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])
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